Data Scientist Bootcamp capstone project

In this project, a RFM model is implemented to relate to customers in each segment. Assessed the Data Quality, performed EDA using Python .

The idea of this challenge was to cluster customers based on a given dataset to align the marketing efforts. Four customer groups were characterized based on income, buying power, credit score, and other criteria

SegmentWise: Unveiling Customer Insights for Exploratory Data Analysis (EDA) and Customer Segmentation

Data Analytics virtual internship programme by KPMG AU on Forage.

Bank Campaign Optimization: Targeting Term Deposit Customers

Data Analysis on Mall customer segmentation using python. In this project, segmented customers using kmeans algorithm and then built a classification model using various algorithms to predict to whcih cluster does a customer belong to.

Customer segmentation using Python and PowerBI

The growth of BNPL services and assess their impact on consumer spending habits and credit risk in the fintech sector

Capstone Project for the Data Scientist Nanodegree by Udacity.

This repository contains data analytics projects that are important to our day to day Machine learning activities

Customer Segmentation Report for Arvato Financial Services. Analyzing demographics data for customers of a mail-order sales company in Germany and identifying most suitable parts of general population whom are most likely to be converted to customers through marketing campaigns.

Data Science - Capstone Project

This project aims to perform customer segmentation on a Mall customer dataset using the K-Means clustering algorithm. The goal of this project is to cluster the customers based on their purchasing behavior and demographic characteristics.

This is a Customer Segmentation model made in Python

The purpose of this project was to perform customer segmentation on mall customers using sklearn Kmeans algorithm. Exploratory data analysis was first performed on the dataset to understand the data. Silhouette analysis was then used to determine the best number of clusters using age, annual income and spending score assigned to customers based …

Using the [Online Retail dataset](https://archive.ics.uci.edu/ml/datasets/Online+Retail) from the UCI Machine Learning Repository for exploratory data analysis, ***Customer Segmentation***, ***RFM Analysis*** and ***Clustering*** with machine learning unsupervised algorithms

Customer Segmentation using RFM analysis

Identifies the parts of the Germany population that best describe the core customer base of the Arvato company. Uses a supervised model to predict which individuals are most likely to convert into becoming customers for the company.

E-commerce site data preparation and sales analysis to answer customer profile and sales questions